Research On Optimal Transmission And Active Security Strategy Of UAV-assisted MEC System

With the rapid development of Internet of Things technology,the number of mobile devices and mobile data traffic are increasing rapidly,which has spawned many emerging applications.These applications usually have computationally intensive and delay-sensitive tasks,which are a huge challenge for resource-constrained mobile devices.Mobile Edge Computing(MEC)technology can help mobile devices offload some or all of the computing-intensive tasks to edge servers.However,traditional MEC cannot be deployed on demand due to the limited service range of fixed locations.With its advantages of low cost,high mobility,and line-of-sight communication,Unmanned Aerial Vehicle(UAV)equipped with MEC servers can freely approach mobile users and provide computing services,which significantly improves network performance.However,the battery capacity of UAV and users is limited,and energy consumption directly affects the execution efficiency of computing tasks,so it is necessary to study the problem of minimizing system energy consumption.In addition,due to the open nature of the wireless network,the wireless communication network is easily accessed by malicious users and used to engage in illegal activities,which has an impact on social and public safety.Therefore,it is also very important to consider the security of the UAV-assisted MEC network.In order to prevent the occurrence of malicious events,it is necessary to conduct legal monitoring on suspicious communication links.Based on the above characteristics of UAV,the combination of UAV and active monitoring technology can effectively improve the monitoring performance.In view of the above situation,the main research contents of this paper are as follows:1.The weighted energy consumption optimization strategy of UAV-assisted MEC system is studied.Multiple users in the system have certain computing task requirements,and the rotor UAV equipped with the MEC server provides them with edge computing services.Considering the situation that the air-ground line-of-sight link is blocked by obstacles,a probabilistic line-of-sight channel that is more in line with the actual scene is adopted.Since the UAV energy consumption is not in the same order of magnitude as the user energy consumption,a weight factor is introduced to balance the air-ground energy consumption.In order to solve the time-continuous domain problem,the path discretization method is used,and then the system weighted energy consumption minimization is realized by joint optimization of time slot,user offload time,computing task assignment,UAV horizontal and vertical trajectory.Considering that the target problem is non-convex,this paper uses convex optimization technology and alternate iterative optimization algorithm to obtain the local optimal solution of the original problem.Simulation results show that the proposed scheme can effectively reduce system energy consumption.2.The active monitoring strategy for UAV-assisted MEC system is studied.There are multiple suspicious users in the system.In order to prevent them from engaging in illegal activities using the UAV equipped with the MEC server,this paper introduces a UAV as a legal monitor to actively monitor the suspicious offloading link in a full-duplex manner.In this system,a joint optimization problem about user unloading time,user local calculation amount,legal monitoring UAV trajectory and interference power is studied.Under the premise of ensuring successful monitoring,the effective monitoring capacity of the legal monitoring UAV is maximized.Since this problem is a non-convex optimization problem,it cannot be directly solved by a convex optimization algorithm.Therefore,this paper adopts methods such as continuous convex approximation and block coordinate descent,and uses alternate iterative algorithm to optimize the solution.The final simulation results prove the superiority of the proposed algorithm,compared with the baseline scheme,the proposed scheme can more effectively improve the monitoring capacity of the legal monitoring UAV.